Softobiz

LLM FINE-TUNING SERVICES

LLM fine-tuning and model evaluation

We assess whether fine-tuning will improve your use case, prepare the training data and evaluate the adapted model against a clear baseline.

  • PEFT first: LoRA and QLoRA adapters, rarely the full model
  • 500 to 5,000 clean examples, curated and held out with you
  • Reproducible pipelines, so re-tuning is a rerun, not a rebuild
WHEN NOT TO FINE-TUNE

Fine-tuning is powerful and frequently misapplied.

If the model keeps getting facts wrong, that is a retrieval problem. Fine-tuning will not fix it, and it will cost you far more to discover that.

So we start with the cheapest lever that works and only move up when the one below genuinely cannot do the job. That discipline saves the budget most teams burn learning it the hard way, and it is the difference between a fine-tune that ships and one that overfits.

RAG for facts. Fine-tune for form.

PROMPT, RAG, FINE-TUNE, DISTILL

Work down the ladder in order.

Only move to the next rung when the one above genuinely cannot do the job.

Prompt engineeringThe base model can already do it with clearer instruction.Lowest. Always start here.
RAGThe answer depends on knowledge that changes or is proprietary.Days to build; low inference cost.
Fine-tuning (LoRA / QLoRA)You need consistent form, tone, or a structured-output behavior the base model will not hold.Roughly 500 to 5,000 clean examples; one-time training.
DistillationYou need frontier quality at small-model latency and cost, at scale.Highest effort; best unit economics at volume.

THE RULE OF THUMB: RAG for facts, fine-tune for form. More noisy data is worse than less clean data, and a small, well-curated set beats a large noisy one every time.

DATA REQUIREMENTS

A data exercise before it is a training exercise.

Quality dominates quantity. We help you assemble, clean, and hold out the dataset that decides the outcome.

FACTOR 01

Volume

The practical sweet spot is roughly 500 to 5,000 clean, representative examples. More noisy data is worse than less clean data.

FACTOR 02

Quality

Consistent formatting, correct labels, and coverage of the behaviors you actually need. Edge cases included, not just the easy ones.

FACTOR 03

Representativeness

Examples must mirror real production inputs. Otherwise the model learns a distribution it will never see.

FACTOR 04

What it cannot fix

Fine-tuning teaches behavior and form, not fresh facts. Knowledge that changes belongs in retrieval.

FACTOR 05

Governance

Data provenance, PII handling, and licensing reviewed up front. Aligned to Responsible AI.

TECHNIQUES AND TOOLS

Parameter-efficient by default.

You rarely need the full model. We favor PEFT and reserve full fine-tuning for the rare case it cannot reach.

01

LoRA

Trains a small adapter, roughly 0.1 to 1 percent of parameters. Fast, cheap, and swappable.

02

QLoRA

A 4-bit quantized base plus LoRA adapters, so you can fine-tune large models on a single GPU.

03

Full fine-tuning

Reserved for the rare case that PEFT cannot reach. Higher cost, higher risk of forgetting.

Training and adaptersHugging Face PEFT / TRL, Axolotl, Unsloth, Predibase
Managed fine-tuningDatabricks, OpenAI, and Azure managed pipelines
ServingAdapters served through the model layer of your AI platform
COST FRAMING

Dominated by data prep and serving, not the training run.

LoRA / QLoRA runEstimate from model size, training data volume, hardware and the number of experiments required.
Full fine-tuningScope training infrastructure, evaluation effort and serving requirements before committing to a budget.
Data preparationOften the largest line. Curation and cleaning dominate.
ServingOngoing, and the surprise cost: an adapter you must host and scale.
Re-tuningRecurs on every base-model upgrade. Budget for it.

The watch-outs are catastrophic forgetting, overfitting, and re-tuning debt each time the base model changes. We design for these before the first run.

HOW OUR FINE-TUNING SERVICES WORK

Five steps, and the first one is the decision.

STEP 01

Decide

Validate that fine-tuning beats prompting or RAG for your case.

STEP 02

Assemble data

Curate, clean, and hold out a representative eval set.

STEP 03

Fine-tune

LoRA or QLoRA, tracked and reproducible.

STEP 04

Evaluate

Score against the base model on real cases via your LLMOps harness.

STEP 05

Serve and maintain

Deploy the adapter and plan for re-tuning on base-model upgrades.

RELATED SERVICES

One lever within the platform.

Fine-tuning sits inside Scaled GenAI and AI Platforms, alongside the rest of the foundation.

FOUNDATION

AI Platform Design and Implementation

The model layer your adapters are served through.

EVALUATION

LLMOps

The harness that scores a fine-tune against the base model.

PREDICTIVE

MLOps

The complement for classical and predictive models.

COMPOSITION

AI Agent Builder

Shared tooling and patterns to compose agents on one foundation.

OPERATING LAYER

GenAI OS

The shared services that run GenAI as a managed capability.

TEAMS

Dedicated Delivery Pod

A team to own fine-tuning and re-tuning as your models change.

FREQUENTLY ASKED QUESTIONS

What ML leads ask us first.

RAG for facts, fine-tune for form. For current or proprietary knowledge, use retrieval. For consistent tone, behavior, or output structure, fine-tune. Often the best answer is a thin adapter on top of RAG, not one instead of the other.

Usually 500 to 5,000 clean, representative examples. Quality matters far more than volume, and a small, well-curated set beats a large noisy one.

You typically re-tune. We build reproducible pipelines so re-tuning on a new base is a rerun, not a rebuild.

DECIDE BEFORE YOU FINE-TUNE

Is fine-tuning the right lever for your case?

Let's confirm whether fine-tuning beats prompting or RAG for your case, and scope it if it is.

Talk to our fine-tuning teamSee the decision ladder